Phenotypic Fit Analysis for Gene Prioritization Beyond Genotype Matching

The patent segments the analysis into two independent parts: genotype analysis and phenotype analysis. The phenotype analysis is further segmented into multiple subscores (HRSS, Jaccard, word2vec, doc2vec) that can be calculated independently. This allows the system to maintain high genotype match accuracy while separately optimizing phenotype-based disease detection, thereby resolving the contradiction between genotype precision and disease detection reliability.

The patent introduces phenotype information as an intermediary between genotype and disease diagnosis. Instead of directly matching genotypes to diseases, the system uses phenotype subscores as a mediator that bridges the gap between genetic data and clinical presentation. This intermediary allows detection of milder diseases that may not have clear genotype matches, resolving the contradiction by enabling phenotype-driven diagnosis complementing genotype-based approaches.

The patent implements self-service through automated literature mining and NLP processing. The system automatically retrieves, formats, and processes scientific literature to extract gene-phenotype associations without requiring manual curation. The NLP models (word2vec, doc2vec) automatically compute similarity subscores, enabling the system to process large datasets independently and rapidly, thereby maintaining high association accuracy while dramatically reducing processing time and labor requirements.

The patent replaces the mechanical manual curation process with automated computational systems. Instead of human experts manually reviewing and curating gene-phenotype associations, the system uses automated literature mining pipelines, NLP algorithms, and machine learning models to perform the same function. This substitution maintains or improves association accuracy while eliminating the time-consuming manual process, directly resolving the contradiction between precision and processing time.

The patent segments the data integration system into distinct modules: literature mining module, NLP processing module, subscore calculation module (HRSS, Jaccard, word2vec, doc2vec), and machine learning module. Each module processes specific data types and performs specific functions. This segmentation allows the system to handle multiple data sources (scientific literature, clinical data, gene databases) independently and systematically, improving comprehensive analysis capability while managing complexity through modular architecture.

The patent creates a universal processing framework that can handle multiple data sources through a common set of tools and algorithms. The NLP models and subscore calculations serve multiple purposes across different data types. The machine learning model integrates all subscores to produce final disease probability predictions. This multi-functional universal system improves adaptability to various data sources while avoiding the complexity of separate specialized systems for each data type.